SkillHack › Career guides › Post-Training Engineer
How to become an Post-Training Engineer
Fine-tuning, RLHF, alignment tuning, and model specialization. Shape base models into products with the behavior you need.
- 5phases in the roadmap
- 15topics to work through
- 75graded practice questions
- Freeno payment, ever
The Post-Training Engineer roadmap
Each topic below carries a study note, real reading, and practice questions in the app. A topic unlocks when you pass the one before it, so the order is the path, so you are never guessing what to learn next.
Phase 1Post-training foundations
Pretraining vs post-training
Pretraining builds broad knowledge and capability from trillions of tokens; post-training is a much smaller phase that shapes behavior, format, and alignment on top.
When to fine-tune vs prompt/RAG
Fine-tuning changes durable behavior and output discipline; prompting steers within existing capability; RAG supplies fresh or large external knowledge at inference.
Data for post-training
Post-training data quality usually beats quantity: a small set of on-distribution, high-quality examples specializes a model better than large generic scrapes or unfiltered synthetic data.
Phase 2Post-training supervised fine-tuning
SFT basics
Supervised fine-tuning trains the model to imitate target assistant responses.
Data curation and quality
Behavioral pathologies in a tuned model (terseness, over-refusal, truncation) usually trace to dataset defects.
Catastrophic forgetting
Narrow, aggressive fine-tuning can erode general capabilities the model is no longer trained on.
Phase 3Post-training preference tuning
RLHF
Classic RLHF has three stages: an SFT checkpoint, a reward model trained on human preference comparisons, and policy optimization (e.g., PPO) against that reward.
DPO and alternatives
Direct Preference Optimization optimizes the policy directly on chosen/rejected pairs against a frozen reference model with a simple classification-style loss.
Reward modeling
A reward model learns human preferences from pairwise comparisons and scores policy outputs, but it is an imperfect proxy.
Phase 4Post-training efficiency
LoRA and PEFT
Parameter-efficient fine-tuning freezes the base weights and trains small add-on parameters.
Quantized training (QLoRA)
QLoRA loads the frozen base weights in 4-bit (e.g., NF4) to slash memory, while training higher-precision LoRA adapters on top.
Distillation
Knowledge distillation transfers a large teacher model's behavior into a smaller student trained on the teacher's outputs or soft targets over your task distribution.
Phase 5Post-training evaluation and production
Evaluating fine-tuned models
Judge a fine-tune on held-out data the model never trained on, compared against the base model and any prior version on task-relevant metrics, plus capability checks.
Detecting regressions
A gain on the primary metric does not guarantee other behaviors held.
Deployment and versioning
Give each checkpoint an immutable version id and record its training data, config, and eval results.
Reading for this path
The primary sources behind the topics above, all free to read.
- Anthropic: Core Views on AI Safety (training overview)
- InstructGPT: Training LMs to follow instructions
- Hugging Face TRL: SFT Trainer docs
- Hugging Face: Alignment Handbook
- Anthropic: Training a helpful and harmless assistant with RLHF
- Direct Preference Optimization (DPO) paper
- Hugging Face TRL: DPO Trainer
- Hugging Face TRL: Reward Modeling
- LoRA: Low-Rank Adaptation of Large Language Models
- Hugging Face PEFT docs
- QLoRA: Efficient Finetuning of Quantized LLMs
- Distilling the Knowledge in a Neural Network
- EleutherAI LM Evaluation Harness
- Hugging Face: Model Cards for versioning and documentation
Coming from another job?
Most people on this path arrived from somewhere else: engineering, analysis, testing, product, support, compliance, design. Onboarding asks what you do today and builds a short starter run out of the gaps, so you begin from what you already know rather than from chapter one. See how it works.
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Start the Post-Training Engineer path for free
The full study notes, the reading, and the practice questions behind every topic above are in the app. Answer one tonight and you have started.
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